Crystal: A Privacy-Preserving Distributed Reputation Management

This paper investigates the situation in which exists the unshared Internet in specific areas while users in there need instant advice from others nearby. Hence, a peer-to-peer network is necessary and established by connecting all neighbouring mobile devices so that they can exchange questions and recommendations. However, not all received recommendations are reliable as users may be unknown to each other. Therefore, the trustworthiness of advice is evaluated based on the advisor's reputation score. The reputation score is locally stored in the user’s mobile device. It is not completely guaranteed that the reputation score is trustful if its owner uses it for a wrong intention. In addition, another privacy problem is about honestly auditing the reputation score on the advising user by the questioning user. Therefore, this work proposes a security model, namely Crystal, for securely managing distributed reputation scores and for preserving user privacy. Crystal ensures that the reputation score can be verified, computed and audited in a secret way. Another significant point is that the device in the peer-to-peer network has limits in physical resources such as bandwidth, power and memory. For this issue, Crystal applies lightweight Elliptic Curve Cryptographic algorithms so that Crystal consumes less the physical resources of devices. The experimental results prove that our proposed model performance is promising.

The Impact of the Quality of Integrated Reporting Disclosure on Corporate Reputation

In 2013, the International Integrated Reporting Council (IIRC) issued the Integrated Reporting (IR) Framework to improve corporate reporting. The purpose of IR is to integrate financial and non-financial information to explain how the organisation creates value and improves firm performance over time. Since the introduction of IR, a company weighs the benefits over the costs of changing to IR. Recent trends in IR have heightened the need to examine the actual contents of IR. The objective of this study was to find the relationship between preparing a high-quality IR and corporate reputation. Corporate reputation is one of the main objectives for the preparation of IR, and preparing a high-quality IR can positively impact corporate reputation. Data was collected from a sample of 120 companies from the IIRC websites for three years (360 firm-year observations) to examine the quality of following the 2013 Framework. An index was developed, and the corporate reputation score was collected from the RepTrak websites. The finding showed that companies preparing high-quality IR reports had a significant positive relationship with corporate reputation. Preparing a high-quality IR sends a signal to users of annual reports as a connotation of the company (good product or services, ethical management reporting), which will help build trust towards the company and improve its reputation. This study contributes by providing empirical evidence for a company, which is uncertain about the benefits of IR. The IR disclosure score can be used as a reference by IIRC and policymakers to prepare the IR. Keywords: integrated reporting, disclosure quality, corporate reputation

Reputation System for Increased Engagement in Public Transport Oriented-Applications

Increasing user engagement is one of the biggest challenges when a new application is developed. An engaged user is one who finds a product valuable; highly engaged users generate profit. This study focuses on increasing user engagement in a transport application, via a user reputation score feature. The score is to reward application users and activity organisers, as well as to motivate beginners by offering a high reputation score in the first days of use. The algorithms are based on exponential and logarithmic functions, and were first tested on synthetic data. Real-world tests have shown that the algorithms behave as expected, but the COVID-19 pandemic created a disturbance which prevented any user from achieving the maximum score and many users from registering altogether. Data show positive results, although the real number of users is not sufficient to certify a correct behaviour. Further tests will be carried out when transport activities return to normal.

Reputation risk indicators of municipal employees

The article discusses the role of psychological indicators of reputational risks in the professional activities of municipal employees. The analysis of foreign and Russian research on the given topic is presented, some topics of related research are shown. The analysis of the sample by sex, length of service and number is illustrated. The procedure for conducting the research is shown, the stages of research, indicators of a complex psychological test are described. Analysis of variance results are also shown. The results of the analysis of motivational, intellectual and personal characteristics of municipal employees holding various leadership positions are presented. Having studied all the results of several stages of the study, it was revealed that significant differences between the motivational, intellectual and personal indicators of managers with different levels of reputation still exist. Executives with a high reputation score are more creative-oriented and forward-looking. Executives with an average reputation score tend to have either average or high scores on various characteristics. Leaders with a low reputation index are more likely to be result-oriented and successful in their activities. Perhaps in the future, with a more detailed and detailed study of these indicators, it will be possible to predict the reputation of this or that leader and identify reputational risks.

Detection of Suspicious or Un-Trusted Users in Crypto-Currency Financial Trading Applications

In this age, where cryptocurrencies are slowly creeping into the banking services and making a name for them, it is becoming crucially essential to figure out the security concerns when users make transactions. This paper investigates the untrusted users of cryptocurrency transaction services, which are connected using smartphones and computers. However, as technology is increasing, transaction frauds are growing, and there is a need to detect vulnerabilities in systems. A methodology is proposed to identify suspicious users based on their reputation score by collaborating centrality measures and machine learning techniques. The results are validated on two cryptocurrencies network datasets, Bitcoin-OTC, and Bitcoin-Alpha, which contain information of the system formed by the users and the user's trust score. Results found that the proposed approach provides improved and accurate results. Hence, the fusion of machine learning with centrality measures provides a highly robust system and can be adapted to prevent smart devices' financial services.

Decision Aids in Online Review Portals: An Empirical Study Investigating Their Effectiveness in the Sensemaking Process of Online Information Consumers

There is an increasing concern about the trustworthiness of online reviews as there is no editorial process for verification of their authenticity. This study investigated the decision-making process of online consumers when reacting to a review, with the reputation score of the reviewer and the number of previous reviews incorporated along with anonymous and non-anonymous reviews. It recruited 200 participants and developed a 3 × 2 × 2 × 2 × 2 mixed experimental study, with the independent variables being the reaction to a review of a restaurant at 3 levels, the reputation score at 2 levels, the number of previous reviews at 2 levels, the valence of the reviews at 2 levels, and the level of anonymity at 2 levels. Five dependent variables were analyzed: level of trust, likelihood of going to the restaurant, a choice question of whether to go to the restaurant, confidence in the decision and the NASA-TLX workload. This study found that the reputation scores complemented the reaction to a review, improving the trust in the information and confidence in the decision made. The findings suggest that incorporating a user rating scale such as the reputation score of a user deters people from writing false or biased reviews and helps improve their accuracy. Although no significant effect of level of anonymity was found in this study, additional personal information about the users writing the review such as photos or other social media links may make a significant difference in the decision-making process.

Building Sentiment Analysis Model and Compute Reputation Scores in E-Commerce Environment Using Machine Learning Techniques

Online reputation systems are a novel and active part of e-commerce environments such as eBay, Amazon, etc. These corporations use reputation reporting systems for trust evaluation by measuring the overall feedback ratings given by buyers, which enables them to compute the reputation score of their products. Such evaluation and computation processes are closely related to sentiment analysis and opinion mining. These techniques incorporate new features into traditional tasks, like polarity detection for positive or negative reviews. The “all excellent reputation” problem is common in the e-commerce domain. Another problem is that sellers can write unfair reviews to endorse or reject any targeted product since a higher reputation leads to higher profits. Therefore, the purpose of the present work is to use a statistical technique for excluding unfair ratings and to illustrate its effectiveness through simulations. Also, the authors have calculated reputation scores from users' feedback based on a sentiment analysis model (SAM). Experimental results demonstrate the effectiveness of the approach.